OpenRaiser/Intern-Atlas huggingface dataset:
DPF-CM: A Data Processing Framework with Privacy-Preserving Vector Databases for Chinese Medical LLMs Training and Deployment
Venue: arXiv cs.LG 2025
Year: 2025

Current open-source training pipelines for Chinese medical language models predominantly emphasize optimizing training methodologies to enhance the performance of large language models (LLMs), yet lack comprehensive exploration into training data processing. To address this gap, we propose DPF-CM, a holistic Data Processing Framework for Chinese Medical LLMs training and deployment. DPF-CM comprises two core modules. The first module is a data processing pipeline tailored for model training. Beyond standard data processing operations, we (1) introduce a chained examples context-learning strategy to generate question-oriented instructions to mitigate the lack of instruction content, and (2) implement an ensemble-based filtering mechanism for preference data curation that averages multiple reward models to suppress noisy samples. The second module focuses on privacy preservation during model deployment. To prevent privacy risks from the inadvertent exposure of training data, we propose a Privacy Preserving Vector Database (PPVD) approach, which involves model memory search, high-risk database construction, secure database construction, and match-and-replace, four key stages to minimize privacy leakage during inference collectively. Experimental results show that DPF-CM significantly improves model accuracy, enabling our trained Chinese medical LLM to achieve state-of-the-art performance among open-source counterparts. Moreover, the framework reduces training data privacy leakage by 27%.
Identity-Preserving Text-to-Video Generation via Training-Free Prompt, Image, and Guidance Enhancement
Venue: arXiv cs.CV 2025
Year: 2025

Identity-preserving text-to-video (IPT2V) generation creates videos faithful to both a reference subject image and a text prompt. While fine-tuning large pretrained video diffusion models on ID-matched data achieves state-of-the-art results on IPT2V, data scarcity and high tuning costs hinder broader improvement. We thus introduce a Training-Free Prompt, Image, and Guidance Enhancement (TPIGE) framework that bridges the semantic gap between the video description and the reference image and design sampling guidance that enhances identity preservation and video quality, achieving performance gains at minimal cost.Specifically, we first propose Face Aware Prompt Enhancement, using GPT-4o to enhance the text prompt with facial details derived from the reference image. We then propose Prompt Aware Reference Image Enhancement, leveraging an identity-preserving image generator to refine the reference image, rectifying conflicts with the text prompt. The above mutual refinement significantly improves input quality before video generation. Finally, we propose ID-Aware Spatiotemporal Guidance Enhancement, utilizing unified gradients to optimize identity preservation and video quality jointly during generation.Our method outperforms prior work and is validated by automatic and human evaluations on a 1000 video test set, winning first place in the ACM Multimedia 2025 Identity-Preserving Video Generation Challenge, demonstrating state-of-the-art performance and strong generality. The code is available at https://github.com/Andyplus1/IPT2V.git.
MetaRoundWorm: A Virtual Reality Escape Room Game for Learning the Lifecycle and Immune Response to Parasitic Infections
Venue: arXiv cs.HC 2025
Year: 2025

Promoting public health is challenging owing to its abstract nature, and individuals may be apprehensive about confronting it. Recently, there has been an increasing interest in using the metaverse and gamification as novel educational techniques to improve learning experiences related to the immune system. Thus, we present MetaRoundWorm, an immersive virtual reality (VR) escape room game designed to enhance the understanding of parasitic infections and host immune responses through interactive, gamified learning. The application simulates the lifecycle of Ascaris lumbricoides and corresponding immunological mechanisms across anatomically accurate environments within the human body. Integrating serious game mechanics with embodied learning principles, MetaRoundWorm offers players a task-driven experience combining exploration, puzzle-solving, and immune system simulation. To evaluate the educational efficacy and user engagement, we conducted a controlled study comparing MetaRoundWorm against a traditional approach, i.e., interactive slides. Results indicate that MetaRoundWorm significantly improves immediate learning outcomes, cognitive engagement, and emotional experience, while maintaining knowledge retention over time. Our findings suggest that immersive VR gamification holds promise as an effective pedagogical tool for communicating complex biomedical concepts and advancing digital health education.
Anomaly detection in network flows using unsupervised online machine learning
Venue: arXiv cs.CR 2025
Year: 2025

Nowadays, the volume of network traffic continues to grow, along with the frequency and sophistication of attacks. This scenario highlights the need for solutions capable of continuously adapting, since network behavior is dynamic and changes over time. This work presents an anomaly detection model for network flows using unsupervised machine learning with online learning capabilities. This approach allows the system to dynamically learn the normal behavior of the network and detect deviations without requiring labeled data, which is particularly useful in real-world environments where traffic is constantly changing and labeled data is scarce. The model was implemented using the River library with a One-Class SVM and evaluated on the NF-UNSW-NB15 dataset and its extended version v2, which contain network flows labeled with different attack categories. The results show an accuracy above 98%, a false positive rate below 3.1%, and a recall of 100% in the most advanced version of the dataset. In addition, the low processing time per flow (<0.033 ms) demonstrates the feasibility of the approach for real-time applications.
Enhancing Partially Relevant Video Retrieval with Robust Alignment Learning
Venue: arXiv cs.CV 2025
Year: 2025

Partially Relevant Video Retrieval (PRVR) aims to retrieve untrimmed videos partially relevant to a given query. The core challenge lies in learning robust query-video alignment against spurious semantic correlations arising from inherent data uncertainty: 1) query ambiguity, where the query incompletely characterizes the target video and often contains uninformative tokens, and 2) partial video relevance, where abundant query-irrelevant segments introduce contextual noise in cross-modal alignment. Existing methods often focus on enhancing multi-scale clip representations and retrieving the most relevant clip. However, the inherent data uncertainty in PRVR renders them vulnerable to distractor videos with spurious similarities, leading to suboptimal performance. To fill this research gap, we propose Robust Alignment Learning (RAL) framework, which explicitly models the uncertainty in data. Key innovations include: 1) we pioneer probabilistic modeling for PRVR by encoding videos and queries as multivariate Gaussian distributions. This not only quantifies data uncertainty but also enables proxy-level matching to capture the variability in cross-modal correspondences; 2) we consider the heterogeneous informativeness of query words and introduce learnable confidence gates to dynamically weight similarity. As a plug-and-play solution, RAL can be seamlessly integrated into the existing architectures. Extensive experiments across diverse retrieval backbones demonstrate its effectiveness.
MILO: A Lightweight Perceptual Quality Metric for Image and Latent-Space Optimization
Venue: arXiv cs.CV 2025
Year: 2025

We present MILO (Metric for Image- and Latent-space Optimization), a lightweight, multiscale, perceptual metric for full-reference image quality assessment (FR-IQA). MILO is trained using pseudo-MOS (Mean Opinion Score) supervision, in which reproducible distortions are applied to diverse images and scored via an ensemble of recent quality metrics that account for visual masking effects. This approach enables accurate learning without requiring large-scale human-labeled datasets. Despite its compact architecture, MILO outperforms existing metrics across standard FR-IQA benchmarks and offers fast inference suitable for real-time applications. Beyond quality prediction, we demonstrate the utility of MILO as a perceptual loss in both image and latent domains. In particular, we show that spatial masking modeled by MILO, when applied to latent representations from a VAE encoder within Stable Diffusion, enables efficient and perceptually aligned optimization. By combining spatial masking with a curriculum learning strategy, we first process perceptually less relevant regions before progressively shifting the optimization to more visually distorted areas. This strategy leads to significantly improved performance in tasks like denoising, super-resolution, and face restoration, while also reducing computational overhead. MILO thus functions as both a state-of-the-art image quality metric and as a practical tool for perceptual optimization in generative pipelines.
Quantum Petri Nets with Event Structure semantics
Venue: arXiv cs.LO 2025
Year: 2025

Classical Petri nets provide a canonical model of concurrency, with unfolding semantics linking nets, occurrence nets, and event structures. No comparable framework exists for quantum concurrency: existing ''quantum Petri nets'' lack rigorous concurrent and sound quantum semantics, analysis tools, and unfolding theory. We introduce Quantum Petri Nets (QPNs), Petri nets equipped with a quantum valuation compatible with the quantum event structure semantics of Clairambault, De Visme, and Winskel (2019). Our contributions are: (i) a local definition of Quantum Occurrence Nets (LQONs) compatible with quantum event structures, (ii) a construction of QPNs with a well-defined unfolding semantics, (iii) a compositional framework for QPNs. This establishes a semantically well grounded model of quantum concurrency, bridging Petri net theory and quantum programming.
Non Technical Debt in Agile Software Development
Venue: arXiv cs.SE 2025
Year: 2025

NonTechnical Debt (NTD) is a common challenge in agile software development, manifesting in four critical forms, Process Debt, Social Debt, People Debt, Organizational debt. NODLA project is a collaboration between Karlstad University and four leading Swedish industrial partners, reveals how various debt types disrupt large scale Agile Software Development (ASD) environments. Through extensive surveys, indepth interviews, and statistical analyses involving a diverse group of software professionals, we identified key drivers of NTD and their impacts. Our findings emphasize (1) Well structured, highly cohesive teams learn faster, adapt more effectively, and innovate consistently. (2) Psychological safety, fostered by proactive leadership, is essential for innovation, experimentation, and keeping employees. (3) Inefficient processes and unclear roles contribute significantly to drops in job satisfaction, productivity and team morale. (4) Social fragmentation, particularly in remote and hybrid settings, breeds rework, delays, and increased costs. (5) Neglected human resource needs, such as delayed hiring or insufficient training, limit an organization ability to meet growing demands. This white paper distils these insights into practical, evidence based strategies, such as refining team composition, clarifying roles, fostering psychological safety, streamlining workflows, and embracing failure as a learning tool. By implementing these strategies, organizations can reduce NTD, reclaim agility, and unlock their teams full potential.
Analyzing Reluctance to Ask for Help When Cooperating With Robots: Insights to Integrate Artificial Agents in HRC
Venue: arXiv cs.RO 2025
Year: 2025

As robot technology advances, collaboration between humans and robots will become more prevalent in industrial tasks. When humans run into issues in such scenarios, a likely future involves relying on artificial agents or robots for aid. This study identifies key aspects for the design of future user-assisting agents. We analyze quantitative and qualitative data from a user study examining the impact of on-demand assistance received from a remote human in a human-robot collaboration (HRC) assembly task. We study scenarios in which users require help and we assess their experiences in requesting and receiving assistance. Additionally, we investigate participants' perceptions of future non-human assisting agents and whether assistance should be on-demand or unsolicited. Through a user study, we analyze the impact that such design decisions (human or artificial assistant, on-demand or unsolicited help) can have on elicited emotional responses, productivity, and preferences of humans engaged in HRC tasks.
TREBL -- A Relative Complete Temporal Event-B Logic. Part I: Theory
Venue: arXiv cs.LO 2025
Year: 2025

The verification of liveness conditions is an important aspect of state-based rigorous methods. This article addresses the extension of the logic of Event-B to a powerful logic, in which properties of traces of an Event-B machine can be expressed. However, all formulae of this logic are still interpreted over states of an Event-B machine rather than traces. The logic exploits that for an Event-B machine $M$ a state $S$ determines all traces of $M$ starting in $S$. We identify a fragment called TREBL of this logic, in which all liveness conditions of interest can be expressed, and define a set of sound derivation rules for the fragment. We further show relative completeness of these derivation rules in the sense that for every valid entailment of a formula $φ$ one can find a derivation, provided the machine $M$ is sufficiently refined. The decisive property is that certain variant terms must be definable in the refined machine. We show that such refinements always exist. Throughout the article several examples from the field of security are used to illustrate the theory.
Do Retrieval Augmented Language Models Know When They Don't Know?
Venue: arXiv cs.CL 2025
Year: 2025

Existing large language models (LLMs) occasionally generate plausible yet factually incorrect responses, known as hallucinations. Two main approaches have been proposed to mitigate hallucinations: retrieval-augmented language models (RALMs) and refusal post-training. However, current research predominantly focuses on their individual effectiveness while overlooking the evaluation of the refusal capability of RALMs. Ideally, if RALMs know when they do not know, they should refuse to answer.In this study, we ask the fundamental question: Do RALMs know when they don't know? Specifically, we investigate three questions. First, are RALMs well calibrated with respect to different internal and external knowledge states? We examine the influence of various factors. Contrary to expectations, when all retrieved documents are irrelevant, RALMs still tend to refuse questions they could have answered correctly. Next, given the model's pronounced \textbf{over-refusal} behavior, we raise a second question: How does a RALM's refusal ability align with its calibration quality? Our results show that the over-refusal problem can be mitigated through in-context fine-tuning. However, we observe that improved refusal behavior does not necessarily imply better calibration or higher overall accuracy. Finally, we ask: Can we combine refusal-aware RALMs with uncertainty-based answer abstention to mitigate over-refusal? We develop a simple yet effective refusal mechanism for refusal-post-trained RALMs that improves their overall answer quality by balancing refusal and correct answers. Our study provides a more comprehensive understanding of the factors influencing RALM behavior. Meanwhile, we emphasize that uncertainty estimation for RALMs remains an open problem deserving deeper investigation.
Agentic Workflow for Education: Concepts and Applications
Venue: arXiv cs.CY 2025
Year: 2025

With the rapid advancement of Large Language Models (LLMs) and Artificial Intelligence (AI) agents, agentic workflows are showing transformative potential in education. This study introduces the Agentic Workflow for Education (AWE), a four-component model comprising self-reflection, tool invocation, task planning, and multi-agent collaboration. We distinguish AWE from traditional LLM-based linear interactions and propose a theoretical framework grounded in the von Neumann Multi-Agent System (MAS) architecture. Through a paradigm shift from static prompt-response systems to dynamic, nonlinear workflows, AWE enables scalable, personalized, and collaborative task execution. We further identify four core application domains: integrated learning environments, personalized AI-assisted learning, simulation-based experimentation, and data-driven decision-making. A case study on automated math test generation shows that AWE-generated items are statistically comparable to real exam questions, validating the model's effectiveness. AWE offers a promising path toward reducing teacher workload, enhancing instructional quality, and enabling broader educational innovation.
Graph Contrastive Learning versus Untrained Baselines: The Role of Dataset Size
Venue: arXiv cs.LG 2025
Year: 2025

Graph Contrastive Learning (GCL) has emerged as a leading paradigm for self-supervised learning on graphs, with strong performance reported on standardized datasets and growing applications ranging from genomics to drug discovery. We ask a basic question: does GCL actually outperform untrained baselines? We find that GCL's advantage depends strongly on dataset size and task difficulty. On standard datasets, untrained Graph Neural Networks (GNNs), simple multilayer perceptrons, and even handcrafted statistics can rival or exceed GCL. On the large molecular dataset ogbg-molhiv, we observe a crossover: GCL lags at small scales but pulls ahead beyond a few thousand graphs, though this gain eventually plateaus. On synthetic datasets, GCL accuracy approximately scales with the logarithm of the number of graphs and its performance gap (compared with untrained GNNs) varies with respect to task complexity. Moving forward, it is crucial to identify the role of dataset size in benchmarks and applications, as well as to design GCL algorithms that avoid performance plateaus.
Model Unmerging: Making Your Models Unmergeable for Secure Model Sharing
Venue: arXiv cs.LG 2025
Year: 2025

Model merging leverages multiple finetuned expert models to construct a multi-task model with low cost, and is gaining increasing attention. However, as a growing number of finetuned models become publicly available, concerns about the safety of model merging have emerged. Unauthorized merging may infringe on developers' rights and risk leaking sensitive personal information. Most existing methods focus on detecting whether a merged model originates from a specific source model, but fail to effectively prevent illegal merging. In this paper, we propose MergeLock, an active protection mechanism that disrupts model parameters to render them unmergeable, thereby directly preventing unauthorized model merging. Specifically, leveraging the inherent symmetry of the attention mechanism in Transformer-based models, we randomly sample two pairs of invertible matrices and apply them to the Query-Key (QK) and Value-Output (VO) branches. This transformation keeps the model's output unchanged while pushing it away from the shared parameter space of other finetuned models. Extensive experiments across both vision and language tasks demonstrate that MergeLock can degrade the performance of merged models by over 95% when a protected model is involved in most cases, demonstrating its effectiveness. Moreover, we further demonstrate that merged models protected by MergeLock cannot be effectively recovered using low-cost restoration methods, further enhancing robustness against unauthorized merging. The code is available at https://github.com/hetailang/Merge-Lock.
Acoustic Interference Suppression in Ultrasound images for Real-Time HIFU Monitoring Using an Image-Based Latent Diffusion Model
Venue: arXiv cs.CV 2025
Year: 2025

High-Intensity Focused Ultrasound (HIFU) is a non-invasive therapeutic technique widely used for treating various diseases. However, the success and safety of HIFU treatments depend on real-time monitoring, which is often hindered by interference when using ultrasound to guide HIFU treatment. To address these challenges, we developed HIFU-ILDiff, a novel deep learning-based approach leveraging latent diffusion models to suppress HIFU-induced interference in ultrasound images. The HIFU-ILDiff model employs a Vector Quantized Variational Autoencoder (VQ-VAE) to encode noisy ultrasound images into a lower-dimensional latent space, followed by a latent diffusion model that iteratively removes interference. The denoised latent vectors are then decoded to reconstruct high-resolution, interference-free ultrasound images. We constructed a comprehensive dataset comprising 18,872 image pairs from in vitro phantoms, ex vivo tissues, and in vivo animal data across multiple imaging modalities and HIFU power levels to train and evaluate the model. Experimental results demonstrate that HIFU-ILDiff significantly outperforms the commonly used Notch Filter method, achieving a Structural Similarity Index (SSIM) of 0.796 and Peak Signal-to-Noise Ratio (PSNR) of 23.780 compared to SSIM of 0.443 and PSNR of 14.420 for the Notch Filter under in vitro scenarios. Additionally, HIFU-ILDiff achieves real-time processing at 15 frames per second, markedly faster than the Notch Filter's 5 seconds per frame. These findings indicate that HIFU-ILDiff is able to denoise HIFU interference in ultrasound guiding images for real-time monitoring during HIFU therapy, which will greatly improve the treatment precision in current clinical applications.
Structured AI Decision-Making in Disaster Management
Venue: arXiv cs.AI 2025
Year: 2025

With artificial intelligence (AI) being applied to bring autonomy to decision-making in safety-critical domains such as the ones typified in the aerospace and emergency-response services, there has been a call to address the ethical implications of structuring those decisions, so they remain reliable and justifiable when human lives are at stake. This paper contributes to addressing the challenge of decision-making by proposing a structured decision-making framework as a foundational step towards responsible AI. The proposed structured decision-making framework is implemented in autonomous decision-making, specifically within disaster management. By introducing concepts of Enabler agents, Levels and Scenarios, the proposed framework's performance is evaluated against systems relying solely on judgement-based insights, as well as human operators who have disaster experience: victims, volunteers, and stakeholders. The results demonstrate that the structured decision-making framework achieves 60.94% greater stability in consistently accurate decisions across multiple Scenarios, compared to judgement-based systems. Moreover, the study shows that the proposed framework outperforms human operators with a 38.93% higher accuracy across various Scenarios. These findings demonstrate the promise of the structured decision-making framework for building more reliable autonomous AI applications in safety-critical contexts.